For many large enterprise transformation efforts, migration of data from legacy systems to a future-state environment (that is, the target environment) is a critical project component, because having actual source data in the target environment is often a pre-requisite for productive integration testing of a new technology solution. This data migration effort is organized by use of constructs known as conversion objects. Each conversion object pulls data from one or more sources (for example, source databases). During data migration, the data corresponding to each conversion object is transformed based on “conversion rules” so that data which was in a suitable form and format for the old environment (that is, the source environment) will be transformed into a form and format suitable for the new target environment. After and/or during transformation, the transformed data is loaded into the target system (for example, one or more target databases).
Conversion objects are designed, developed, and tested in parallel. There are often dependencies between conversion objects. When conversion object A is dependent upon conversion object B then this means that conversion object A should be fully extracted, fully transformed and fully loaded before conversion object B is transformed and/or loaded.
Project managers often look to team leads for the status of each conversion object, dependencies between conversion objects, and/or impacts of incomplete objects. For smaller initiatives that do not have a dedicated data team, the additional responsibility of managing conversion objects often falls on the process team lead(s). Larger initiatives with a dedicated conversion manager may use multiple landscapes to support multiple testing scenarios or roll-outs, requiring the correct data be loaded into the correct environment at the correct time. These variables add to the complexity and risk of the data migration workstream. Organization, status, and progress tracking of all these moving parts is thus often times a challenge, which can lead to budget overruns from both a time and money standpoint.
Various workers typically play certain roles in a typical large data migration project. A data lead type worker: (i) tracks workload by resource and execution times by data object, identifies risk, accelerates performance where required, improves data quality, and provides leadership on technical activities for data conversions; and (ii) is responsible for the data team's adherence to the overall project plan, including design and development of data objects and associated testing (for smaller projects, these responsibilities may be shared across the process team leads as there may not be a dedicated data lead). A project manager type worker: (i) manages scope, cost, schedule, deliverables, project resources and communications plans; and (ii) provides day-to-day direction to the project team. A cutover lead type worker ensures the conversion effort fits into the cutover window, negotiates legacy system outages, and plans cutovers. A functional data analyst type worker: (i) “owns” one or more conversion objects from a functional standpoint and ensures communications between business and information technology groups; and (ii) is accountable for functional specification of the conversion and functional unit testing, and coordinates post-load validations. A developer type worker: (i) “owns” one or more conversion objects from a technical standpoint; and (ii) is accountable for technical specifications on ETL design and development of ETL code.
SAP ERP (herein sometimes simply referred to as “SAP”) is SAP AG's Enterprise Resource Planning software. SAP incorporates the key business functions of an organization. SAP includes functionality in many areas, such as accounts payable, accounts receivable, accounting and financial reporting, risk management, regulatory compliance, and so on. SAP includes several modules, including: (i) utilities for marketing and sales; (ii) field service; (iii) product design and development; (iv) production and inventory control; (v) human resources; and (vi) finance and accounting. SAP obtains data from the various modules to provide the company or organization with enterprise resource planning capabilities. SAP can potentially be used by an enterprise to go from an older calculations system to a fully integrated software package. (Note: the term(s) “SAP”, “SAP ERP” and/or “SAP ENTERPRISE RESOURCE PLANNING” may be subject to trademark rights in various jurisdictions throughout the world and are used here only in reference to the products or services properly denominated by the marks to the extent that such trademark rights may exist.)
SAP is used in this document as a non-limiting example of a data migration target system. Other non-limiting example targets include SAP CRM and systems from salesforce.com, among others. (Note: the term(s) “SAP CRM”, “SAP CUSTOMER RELATIONSHIP MANAGEMENT”, and/or “SALESFORCE.COM” may be subject to trademark rights in various jurisdictions throughout the world and are used here only in reference to the products or services properly denominated by the marks to the extent that such trademark rights may exist.)